Assessing the WMC-RTDETR methodology for other agricultural crops

The gap

Evidence profile

Sourced from the future work and abstract and future-work section and stated research gap of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 6 journals. Those papers have been cited 60 times in total.

Supporting evidence — 8 representative gaps

• AI-Driven Crop Disease Prediction System (2026) · International Journal of Science, Strategic Management and Technology · doi

  The AI-Driven Crop Disease Prediction System bridges conventional agricultural practices with advanced automation. By combining computer vision, deep learning, and environmental data analysis, the system provides farmers with an efficient, accurate, and accessible solution for early disease identification. It reduces dependency on manual expert inspection, supports localized decision-making, and promotes sustainable crop management through timely intervention and prevention strategies. Future work will focus on the following enhancements: • Blockchain-based record storage to ensure transparency and traceability of disease data. • Federated AI learning models for privacy-preserving and region-specific training. • Integration of conversational chatbots to assist farmers with instant, context-aware recommendations. • Predictive modeling for disease outbreak forecasting using weather and soil parameters. • IoT-based real-time monitoring for automated image and sensor data collection. • Cloud-based synchronization for scalable deployment across multiple agricultural zones. © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution. https://ijsmt.org/ , Email: [email protected] 6 International Journal of Science, Strategic Management and Technology Volume 02 Issue 05 May-2026 | ISSN: 3108-1762 (Online) | Impact Factor: 3.8 An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases

  generalfuture work
  Keywords: disease based crop system agricultural learning farmers accessible management peer reviewed openly ijsmt international journal
• Improving food security through indoor vertical farming of microgreens: a trendy fad or a promising solution? A review (2026) · Frontiers in Sustainable Food Systems · doi

  IVF and microgreens have strong potential to contribute to food security, representing a transformative agricultural approach through environmental control and advanced technologies for efficient and sustainable food production. These advantages are particularly rele- vant in densely populated areas, regions with limited arable land, or locations with fragile food supply chains. However, several challenges remain, including high energy costs, limited crop variability, and uncertain consumer acceptance. In addition, the lack of standardized cultivation practices and quality control measures for microgreen pro- duction represents a significant obstacle. Establishing industry-wide standards for substrates, seed selection, nutrient solutions, and envi- ronmental parameters will be essential to ensure consistent product quality and support economically viable, large-scale operations. Addressing these challenges will require coordinated efforts involving technological innovation, regulatory support, and integration with conventional agricultural practices. Future research should adopt a systems-level perspective to address the key constraints limiting the scalability of IVF for micro- green production. In particular, improving energy efficiency remains a priority, requiring advances in renewable energy integration, LED optimization, and climate control systems, while balancing resource inputs with crop productivity and quality. The integration of advanced digital technologies, including AI, sensor networks, and predictive modeling, offers opportunities to enhance automation, real-time monitoring, and resource-use efficiency, although their application remains fragmented. Expanding crop diversity through the develop- ment of varieties adapted to controlled environments, together with targeted breeding and biofortification strategies, will be critical to

  generalfuture work
  Keywords: food control energy crop quality integration agricultural advanced technologies production limited challenges including practices support
• Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production (2026) · Plants · doi

  Artificial intelligence has demonstrated great potential in crop production, covering biotic stress monitoring, soil health management, precision operation, supply chain opti- mization, and climate-resilient agriculture. However, challenges such as data scarcity, in- sufficient generalization, and limited field deployment still restrict large-scale applications. Future research can be systematically divided into short-term technical improvements, long-term research opportunities, and practical implementation challenges, with a focus on integrating foundation models, large-scale agricultural datasets, multimodal phenotyping platforms, domain adaptation, and lightweight edge-AI systems into crop production. 5.1. Short-Term Technical Improvements In the short term, efforts should focus on addressing immediate technical bottlenecks and enhancing the practicality of current AI systems. Lightweight and interpretable AI techniques will be prioritized for edge deployment, while multimodal fusion and small- sample learning will improve performance under complex field conditions. Lightweight and edge-deployable AI systems are essential for real-time field appli- cations. Current deep learning models, including CNNs and transformers, often have high computational costs, limiting their use on UAVs, ground robots, and portable devices. Short-term work should optimize lightweight architectures such as MobileNet, YOLOv3- Tiny, and compressed ViT variants, combining pruning, quantization, and knowledge distillation to balance accuracy and speed [53,64]. These lightweight models can be directly deployed for real-time disease and pest detection in field environments. Multimodal data fusion will further enhance monitoring robustness. Integrating RGB, multispectral, hyperspectral, and LiDAR data can capture multi-scale crop information under variable lighting, weather, and canopy occlusion [12,15]. Combining IoT sensor data with remote sensing data can improve early warning accuracy for diseases and pests, supporting data-driven decision-making in precision agriculture. Domain adaptation and transfer learning will mitigate cross-environment general- ization gaps. Models trained in specific regions often fail in new fields due to domain shift [15,65]. Short-term optimization of adversarial transfer learning and style normaliza- tion can improve model adaptability across different crops, regions, and growth stages, reducing reliance on large labeled datasets [66]. https://doi.org/10.3390/plants15121863 Plants 2026, 15, 1863 13 of 19 5.2. Long-Term Research Opportunities In the long term, research will focus on foundational innovation and systematic integration, promoting AI from single-point applications to full-chain intelligent solutions. Key directions include foundation models, large-scale datasets, multimodal phenotyping platforms, and cross-domain adaptive systems. Agricultural foundation models will become a core research direction. Integrating larg

  generalfuture work
  Keywords: term models short lightweight field large scale multimodal domain systems learning crop technical long focus
• Application of crop growth models in crop yield assessment (2026) · Frontiers in Plant Science · cited 1× · doi

  Future research should focus on the deep integration of crop growth models with remote sensing, the Internet of Things (IoT), big data, cloud computing, and artificial intelligence technologies to establish intelligent “space-air-ground” decision-making systems that support precision, unmanned, and climate-resilient agriculture.

  generalabstractevidence 5/5
  Keywords: future focus deep integration crop growth models remote sensing internet things cloud computing artificial intelligence
• GreenMind:AI-Powered Smart Farming Ecosystem (2026) · International Journal of Creative and Open Research in Engineering and Management · doi

  Integration of advanced AI and deep learning architectures to improve disease detection accuracy. Implementation of edge computing techniques to allow for local data processing and predictions. Expansion into community farming networks utilizing predictive analytics to provide personalized, seasonal farming recommendations.

  generalfuture-work sectionevidence 5/5
  Keywords: integration advanced deep learning architectures improve disease detection
• Application of crop growth models in crop yield assessment (2026) · Frontiers in Plant Science · cited 1× · doi

  The paper identifies a gap in the integration of machine learning and deep learning techniques with crop growth models. There is a need for region-specific adaptation strategies. The paper highlights the importance of considering local climatic conditions and production systems.

  generalstated research gapevidence 5/5
  Keywords: paper identifies gap integration machine learning deep techniques
• Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops (2024) · Scientific Reports · cited 58× · doi

  The study identifies the need for creative thinking and interdisciplinary cooperation to overcome the obstacles to AI adoption in agriculture. The paper highlights the limitations of current machine learning and deep learning models in agriculture.

  generalstated research gapevidence 5/5
  Keywords: study identifies need creative thinking interdisciplinary cooperation overcome
• Editorial: Plant pest and disease model forecasting: enhancing precise and data-driven agricultural practices (2026) · Frontiers in Plant Science · doi

  assessing the WMC-RTDETR methodology for other agricultural crops, - exploring improvements in deep learning in real-world field environments, - developing more efficient and accurate methods for detecting pests and diseases

  generalfuture-work sectionevidence 5/5
  Keywords: assessing wmc-rtdetr methodology other agricultural crops exploring improvements

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